The present invention relates to evaluation of network performance and resource allocation. Specifically, worst-case queuing delay for aggregates of traffic can be measured using a measurement based traffic profile. Periodic worst-case delay under hypothetical traffic conditions and allocations of bandwidth can be estimated.
A key measure of network performance is delay. The average delay in any acceptable modern day network is low, and the exact value of the average typically is of limited use in comparing similar services. In addition, averaging masks extreme values, but the extreme high values (occasional large delays) can be very annoying to a typical user. That is, in evaluating actual user experience, the periodic worst-case delay estimation can be important.
Average delay can be measured by exchanging probe packets between each of a specified pair of routers using a tool, such as Cisco's Service Assurance Agent. Another tool, such as Cisco's Internet Performance Monitor, can be used to collect and process the data obtained and make it available to other applications. Such end-to-end probing schemes have several drawbacks, including increasing as the square of the number of nodes in the network and consequently not scalable to large N; also, the probes themselves use network resources and therefore can affect performance. In addition, end-to-end measurements schemes do not provide a periodic worst-case delay.
Average delay and periodic worst-case delay can be obtained by direct measurement. Every packet passing through a queue in a time interval can be directly monitored to determine packet delay. For example, a time stamp can be inserted into the header of each packet upon arrival. The delay until the packet leaves the queue can be calculated by monitoring when the packet leaves the queue and comparing it to the time stamped arrival time. The delays for all packets can be averaged to give an average delay for the time interval. The largest delay can be identified as the actual periodic worst-case delay. However, direct measurement is cumbersome.
A further limitation of both end-to-end probing schemes and direct measurement is that they only provide information about current conditions. They do not predict how the system will perform under different traffic conditions, or how the system will perform with a different allocation of resources. Being able to analyze network performance under hypothetical conditions would be useful, for example when a customer and internet service provider agree to the customer sending increased voice and video traffic. Such traffic is burstier than data traffic. It would be useful to be able to estimate the effect of an increase in bursty traffic on delay. Also, it would be useful to be able to tell how much additional bandwidth is needed to achieve a certain reduction in delay with existing traffic.
Accordingly, it would be useful to conveniently obtain an estimate of periodic worst-case delay, in a way that is scalable to large networks, and in a way that does not disrupt normal network performance. It would further be desirable for the method to be rapidly adaptable to real time changes in traffic conditions. It would be useful to be able to analyze worst-case delay under hypothetical conditions such as different output link bandwidth allocations.